MGANSL: multi-network representation generating with generative adversarial network for synthetic lethality prediction.

ABSTRACT:

Background

Cancer is a complex disease that arises from the simultaneous mutations of multiple biological molecules. An effective therapeutic strategy is to exploit synthetic lethality (SL) by targeting the SL partner of cancer driver genes. Computational approaches have emerged as efficient complements to traditional methods. Although some methods integrate heterogeneous sources to learn multi-network representations, they often neglect consistent information shared across different networks and specific characteristic specific to individual network. Therefore, a comprehensive representation learning framework for capturing both multi-network consistency and network-specific information of gene pair is needed.

Results

We proposed a novel approach capturing Multi-network con

SUBMITTER: Li J 

PROVIDER: S-EPMC12860022 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature